Deep learning models have become the mainstream algorithm for processing computer vision tasks. In tasks of object detection, detection box is usually set as a rectangular aligned with coordinate axis, so to achieve complete packaging object. However, when facing some objects large aspect ratio and angle, bounding must be enlarged, which makes contain amount useless background information. this...

Despite of the substantial progress visual object detection, models trained in one video domain often fail to generalize well others due change camera configurations, lighting conditions, and person views. In this paper, we present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training adaptive detectors. DC is deduced from error bound minimization p...

Despite the continued successes of computationally efficient deep neural network architectures for video object detection, performance continually arrives at great trilemma speed versus accuracy computational resources (pick two). Current attempts to exploit temporal information in data overcome this are bottlenecked by state art detection models. This work presents motion vector extrapolation ...

In recent years there is rapid improvement in Object detection areas of video analysis and image processing applications. Determing a desired object became an important aspect, so that are many numerous methods evolved detection. this regard as development Deep Learning for its high-level processing, extracting deeper features, reliable flexible compared to conventional techniques. article, the...

As one of the fundamental components object detection, intersection-over-union (IoU) calculations between two bounding boxes play an important role in samples selection, NMS operation and evaluation detection algorithms. This procedure is well-defined solved for planar images, while it challenging spherical ones. Some existing methods utilize to represent objects. However, they are biased due d...